Open Access

Online Signature Verification Using Fourier Descriptors

EURASIP Journal on Advances in Signal Processing20092009:260516

DOI: 10.1155/2009/260516

Received: 27 October 2008

Accepted: 25 July 2009

Published: 16 September 2009

Abstract

We present a novel online signature verification system based on the Fast Fourier Transform. The advantage of using the Fourier domain is the ability to compactly represent an online signature using a fixed number of coefficients. The fixed-length representation leads to fast matching algorithms and is essential in certain applications. The challenge on the other hand is to find the right preprocessing steps and matching algorithm for this representation. We report on the effectiveness of the proposed method, along with the effects of individual preprocessing and normalization steps, based on comprehensive tests over two public signature databases. We also propose to use the pen-up duration information in identifying forgeries. The best results obtained on the SUSIG-Visual subcorpus and the MCYT-100 database are 6.2% and 12.1% error rate on skilled forgeries, respectively. The fusion of the proposed system with our state-of-the-art Dynamic Time Warping (DTW) system lowers the error rate of the DTW system by up to about 25%. While the current error rates are higher than state-of-the-art results for these databases, as an approach using global features, the system possesses many advantages. Considering also the suggested improvements, the FFT system shows promise both as a stand-alone system and especially in combination with approaches that are based on local features.

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Authors’ Affiliations

(1)
Faculty of Engineering and Natural Sciences, Sabanci University
(2)
National Research Institute of Electronics and Cryptology (UEKAE), Scientific and Technological Research Council of Turkey (TUBITAK)

Copyright

© B. Yanikoglu and A. Kholmatov. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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